code: def forward(self, inputs, encoder_word_pos, gsrm_word_pos):
      b, c, h, w = inputs.shape
      conv_features = paddle.reshape(inputs, shape=[-1, c, h * w])
      conv_features = paddle.transpose(conv_features, perm=[0, 2, 1])
      # transformer encoder
      b, t, c = conv_features.shape
  
      enc_inputs = [conv_features, encoder_word_pos, None]
      word_features = self.wrap_encoder_for_feature(enc_inputs)
  
      # pvam
      b, t, c = word_features.shape
      word_features = self.fc0(word_features)
      word_features_ = paddle.reshape(word_features, [-1, 1, t, c])
      word_features_ = paddle.tile(
          word_features_, [1, self.max_length, 1, 1]
      )
      word_pos_feature = self.emb(gsrm_word_pos)
      word_pos_feature_ = paddle.reshape(
          word_pos_feature, [-1, self.max_length, 1, c]
      )
      word_pos_feature_ = paddle.tile(word_pos_feature_, [1, 1, t, 1])
      y = word_pos_feature_ + word_features_
      y = F.tanh(y)
      attention_weight = self.fc1(y)
      attention_weight = paddle.reshape(
          attention_weight, shape=[-1, self.max_length, t]
      )
      attention_weight = F.softmax(attention_weight, axis=-1)
      pvam_features = paddle.matmul(
          attention_weight, word_features
      )  # [b, max_length, c]
      return pvam_features
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  from paddle import nn, ParamAttr
  from paddle.nn import functional
  import numpy
  from .self_attention import WrapEncoderForFeature
  from .self_attention import WrapEncoder
  from paddle.static import Program
  from ppocr.modeling.backbones.rec_resnet_fpn import ResNetFPN
  from collections import OrderedDict
label_desc: Use image node_id 2 for calling the PVAM obj's underlying member method code with example usage: obj.forward(inputs, encoder_word_pos, gsrm_word_pos) and returns: pvam_features